Daily TMLR digest for Feb 16, 2026
TMLR
Accepted papers
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Title: Introducing Background Temperature to Characterise Hidden Randomness in Large Language Models
Authors: Alberto Messina, Stefano Scotta
Abstract: Even when decoding with temperature $T=0$, large language models (LLMs) can produce divergent outputs for identical inputs. Recent works align in highlighting implementation-level sources of nondeterminism, including batch-size variation, kernel non-invariance, and floating-point non-associativity. In this work, we formalize this behavior by introducing the notion of background temperature $T_{\mathrm{bg}}$, the effective temperature induced by an implementation-dependent perturbation process observed even when nominal $T=0$. We provide clean definitions, show how $T_{\mathrm{bg}}$ relates to a stochastic perturbation governed by the inference environment $I$, and propose an empirical protocol to estimate $T_{bg}$ via the equivalent temperature $T_n(I)$ of an ideal reference system. We conclude with a set of pilot experiments run on a representative pool from the major LLM providers that demonstrate the idea and outline implications for reproducibility, evaluation, and deployment.
URL: https://openreview.net/forum?id=bz0he4bARF
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Title: Density-Aware Farthest Point Sampling
Authors: Paolo Climaco, Jochen Garcke
Abstract: We focus on training machine learning regression models in scenarios where the availability of labeled training data is limited due to computational constraints or high labeling costs. Thus, selecting suitable training sets from unlabeled data is essential for balancing performance and efficiency. For the selection of the training data, we focus on passive and model-agnostic sampling methods that only consider the data feature representations. We derive an upper bound for the expected prediction error of Lipschitz continuous regression models that linearly depends on the weighted fill distance of the training set—a quantity we can estimate simply by considering the data features. We introduce ``Density-Aware Farthest Point Sampling'' (DA-FPS), a novel sampling method. We prove that DA-FPS provides approximate minimizers for a data-driven estimation of the weighted fill distance, thereby aiming at minimizing our derived bound. We conduct experiments using two regression models across three datasets. The results demonstrate that DA-FPS significantly reduces the mean absolute prediction error compared to other sampling strategies.
URL: https://openreview.net/forum?id=vI47lgIfYc
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Title: Delta-Influence: Identifying Poisons via Influence Functions
Authors: Wenjie Li, Jiawei Li, Pengcheng Zeng, Christian Schroeder de Witt, Ameya Prabhu, Amartya Sanyal
Abstract: Addressing data integrity challenges, such as unlearning the effects of targeted data poisoning after model training, is necessary for the reliable deployment of machine learning models. State-of-the-art influence functions, such as EK-FAC and TRAK, often fail to accurately attribute abnormal model behavior to the specific poisoned training data responsible for the data poisoning attack. In addition, traditional unlearning algorithms often struggle to effectively remove the influence of poisoned samples, particularly when only a few affected examples can be identified. To address these challenge, we introduce $\Delta$-Influence, a novel approach that leverages influence functions to trace abnormal model behavior back to the responsible poisoned training data using just one poisoned test example, without assuming any prior knowledge of the attack. $\Delta$-Influence applies data transformations that sever the link between poisoned training data and compromised test points without significantly affecting clean data. This allows detecting large negative shifts in influence scores following data transformations, a phenomenon we term as influence collapse, thereby accurately identifying poisoned training data. Unlearning this subset, e.g. through retraining, effectively eliminates the data poisoning. We validate our method across three vision-based poisoning attacks and three datasets, benchmarking against five detection algorithms and five unlearning strategies. We show that $\Delta$-Influence consistently achieves the best unlearning across all settings, showing the promise of influence functions for corrective unlearning.
URL: https://openreview.net/forum?id=4XtcG8NNaG
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New submissions
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Title: Universal Latent Homeomorphic Manifolds: A Framework for Cross-Domain Representation Unification
Abstract: We present the Universal Latent Homeomorphic Manifold (ULHM), a framework that unifies semantic representations (e.g., human descriptions, diagnostic labels) and observation-driven machine representations (e.g., pixel intensities, sensor readings) into a single latent structure. Despite originating from fundamentally different pathways, both modalities capture the same underlying reality. We establish \emph{homeomorphism}, a continuous bijection preserving topological structure, as the mathematical criterion for determining when latent manifolds induced by different semantic-observation pairs can be rigorously unified. When this homeomorphic criterion is satisfied, it enables three critical applications: (1) semantic-guided sparse recovery from incomplete observations, (2) cross-domain transfer learning with verified structural compatibility, and (3) zero-shot compositional learning via valid transfer from semantic to observation space. Our framework learns continuous manifold-to-manifold transformations through conditional variational inference, with training objectives explicitly designed to enforce bi-Lipschitz homeomorphic properties. We develop practical verification algorithms, including trust, continuity, and Wasserstein distance metrics, that empirically validate whether the learned representations achieve homeomorphic structure from finite samples. Experiments demonstrate substantial improvements over state-of-the-art (SOTA) baselines: (1) sparse recovery from 8\% of pixels with much lower MSE than SOTA on CelebA under noise, (2) cross-domain transfer achieving 86.73\% MNIST$\rightarrow$Fashion-MNIST accuracy without retraining, and (3) zero-shot classification achieving 78.76\% on CIFAR-10, exceeding prior work by 16.66\%. Critically, the homeomorphism criterion determines when different semantic-observation pairs share compatible latent structure, enabling principled unification into universal representations and providing a mathematical foundation for decomposing general foundation models into domain-specific components.
URL: https://openreview.net/forum?id=YoZSpRWhZH
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Title: Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation
Abstract: Accurate segmentation of cancerous lesions from 3D computed tomography (CT) scans is essential for automated treatment planning and response assessment. However, even state-of-the-art models combining self-supervised learning (SSL) pretrained transformers with convolutional decoders are susceptible to out-of-distribution (OOD) inputs, generating confidently incorrect tumor segmentations, posing risks to safe clinical deployment. Existing logit-based methods suffer from task-specific model biases, while architectural enhancements to explicitly detect OOD increase parameters and computational costs. Hence, we introduce a lightweight, plug-and-play post-hoc random forests-based OOD detection framework called RF-Deep that leverages deep features with limited outlier exposure. RF-Deep enhances generalization to imaging variations by repurposing the hierarchical features from the pretrained-then-finetuned backbone, providing task-relevant OOD detection by extracting the features from multiple regions of interest anchored to the predicted tumor segmentations. We compared RF-Deep against existing OOD detection methods using 2,056 CT scans across near-OOD (pulmonary embolism, negative COVID-19) and far-OOD (kidney cancer, healthy pancreas) datasets. RF-Deep achieved AUROC > 93.50 for the challenging near-OOD datasets and near-perfect detection (AUROC > 99.00) for the far-OOD datasets, substantially outperforming logit-based and radiomics approaches. RF-Deep maintained consistent performance across networks of different depths and pretraining strategies, demonstrating its effectiveness as a lightweight, architecture-agnostic approach to enhance the reliability of tumor segmentation from CT volumes.
URL: https://openreview.net/forum?id=XmjYlBxFxn
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Title: Control-oriented Energy-Based Actionable World Model for Decision-Making and Process Control
Abstract: We introduce the \emph{Energy-Based Actionable World Model} (EBAWM), a hybrid
world-modeling framework for industrial process forecasting and control that
combines deterministic state-space dynamics with an energy-based transition
critic. EBAWM is designed for long-horizon, high-stakes decision-making, where
reliable recursive prediction requires both stable state evolution and
principled uncertainty awareness. In contrast to modern deep time-series models—such
as CNNs, RNNs, and Transformers—that operate primarily as input--output predictors, EBAWM
maintains an explicit, recursively propagated state tied to physically
meaningful system variables. This structure enables state correction,
long-horizon simulation, and direct integration with Receding Horizon Control,
model predictive control, and model-based reinforcement learning.The deterministic transition
model provides a strong inductive bias for system
identification by favoring explicit, Markovian, action-conditioned state
transitions, thereby mitigating representation collapse, a common failure mode
in energy-based learning. Uncertainty is captured through an energy function that evaluates the
plausibility of action-conditioned state transitions, rather than by injecting
stochasticity into the dynamics or relying on model ensembles. High-energy
regions naturally indicate dynamically inconsistent or out-of-distribution
behavior, yielding an interpretable uncertainty-aware signal without assuming a
parametric noise model. Our contributions are: (i) we show that the geometry of
the learned energy landscape encodes
dynamical structure and stability-related properties, enabling
uncertainty-aware forecasting and implicit control;(ii) we introduce a
control-oriented world model that combines recursive,
action-conditioned physical state propagation with energy-based transition
evaluation, supporting online optimization and closed-loop decision-making;
and (iii) we propose a simple and stable energy-based modeling design that avoids
representation collapse by operating on a latent space shaped by a
deterministic forecaster.
URL: https://openreview.net/forum?id=JLXdpnjEU3
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Title: Constrained Reinforcement Learning Using Successor Representations
Abstract: Real-world Reinforcement Learning depends on the ability to formulate safety constraints into a policy. Unfortunately, current methods are hard to adapt to changes in the cost function introduced by, e.g., domain shift or obstacles moving over time.
The lack of adaptability means that policies are too unflexible to deal with complex real-world conditions.
We propose the SafeDSR, a novel method that allows quick retraining of policies towards new cost structures by decoupling the dynamics, reward structure, and costs by introducing a single learnable weight matrix. This matrix can be updated in a supervised manner instead of having to adapt the whole network if the cost structure of the environment changes.
We demonstrate this ability in a freely configurable environment and show that our method is competitive with the state of the art while being considerably more flexible. The source code will be made publicly available upon acceptance.
URL: https://openreview.net/forum?id=6zUq7knzwA
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